Transfer Function and Time Series Outlier Analysis: Modelling Soil Salinity in Loamy Sand Soil by Including the Influences of Irrigation Management and Soil Temperature

Aljoumani, Basem; Sánchez-Espigares, José A.; Cañameras, Núria; Wessolek, Gerd; Josa, Ramón

doi:10.1002/ird.2187

Cited by 8 publications

(6 citation statements)

References 34 publications

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“…Jien and Wang ( 2013 ) also reported an increment in the soil CEC value by application of BC having high porosity and large surface area. The sandy loam contains low mineral contents due to lesser silt and clay fractions; thus, BC acquires a high surface charge in sandy loam soil compared to clay soil (Aljoumani et al, 2017 ). Similarly, the CEC percentage increase was higher for sandy loam than clay, as reported by Ghorbani et al ( 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Microbial-assisted soil chromium immobilization through zinc and iron-enriched rice husk biochar

Batool

Rahman²,

Ali³

et al. 2022

Front. Microbiol.

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Soil chromium toxicity usually caused by the tannery effluent compromises the environment and causes serious health hazards. The microbial role in strengthening biochar for its soil chromium immobilization remains largely unknown. Hence, this study evaluated the effectiveness of zinc and iron-enriched rice husk biochar (ZnBC and FeBC) with microbial combinations to facilitate the chromium immobilization in sandy loam soil. We performed morphological and molecular characterization of fungal [Trichoderma harzianum (F1), Trichoderma viride (F2)] and bacterial [Pseudomonas fluorescence (B1), Bacillus subtilis (B2)] species before their application as soil ameliorants. There were twenty-five treatments having ZnBC and FeBC @ 1.5 and 3% inoculated with bacterial and fungal isolates parallel to wastewater in triplicates. The soil analyses were conducted in three intervals each after 20, 30, and 40 days. The combination of FeBC 3%+F2 reduced the soil DTPA-extractable chromium by 96.8% after 40 days of incubation (DAI) relative to wastewater. Similarly, 92.81% reduction in chromium concentration was achieved through ZnBC 3%+B1 after 40 DAI compared to wastewater. Under the respective treatments, soil Cr(VI) retention trend increased with time such as 40 > 30 > 20 DAI. Langmuir adsorption isotherm verified the highest chromium adsorption capacity (41.6 mg g−1) with FeBC 3% at 40 DAI. Likewise, principal component analysis (PCA) and heat map disclosed electrical conductivity-chromium positive, while cation exchange capacity-chromium and pH-organic matter negative correlations. PCA suggested the ZnBC-bacterial while FeBC-fungal combinations as effective Cr(VI) immobilizers with >70% data variance at 40 DAI. Overall, the study showed that microbes + ZnBC/FeBC resulted in low pH, high OM, and CEC, which ultimately played a role in maximum Cr(VI) adsorption from wastewater applied to the soil. The study also revealed the interrelation and alternations in soil dynamics with pollution control treatments. Based on primitive soil characteristics such as soil metal concentration, its acidity, and alkalinity, the selection criteria can be set for treatments application to regulate the soil properties. Additionally, FeBC with Trichoderma viride should be tested on the field scale to remediate the Cr(VI) toxicity.

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Section: Discussionmentioning

confidence: 99%

Microbial-assisted soil chromium immobilization through zinc and iron-enriched rice husk biochar

Batool

Rahman²,

Ali³

et al. 2022

Front. Microbiol.

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“…However, the ARIMA is not suitable to estimate the extreme cases consisting of a nonlinear relationship [17]. The transfer function includes the coefficient of moving average (MA) and autoregressive (AR) [18]. It is a time series approach that can explain the dynamic series case in the model prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Comparison study of transfer function and artificial neural network for cash flow analysis at Bank Rakyat Indonesia

Faricha¹,

Ulyah

Susanti

et al. 2022

IJECE

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<span lang="EN-US">The cash flow analysis is essential to examine the economic flows in the financial system. In this paper, the financial dataset at Bank Rakyat Indonesia was used, it recorded the sources of cash inflow and outflow during a particular period. The univariate time series model like the autoregressive and integrated moving average is the common approach to build the prediction based on the historical dataset. However, it is not suitable to estimate the multivariate dataset and to predict the extreme cases consisting of nonlinear pairs between independent-dependent variables. In this study, the comparison of using two types of models i.e., transfer function and artificial neural network (ANN) were investigated. The transfer function model includes the coefficient of moving average (MA) and autoregressive (AR), which allows the multivariate analysis. Furthermore, the artificial neural network allows the learning paradigm to achieve optimal prediction. The financial dataset was divided into training (70%) and testing (30%) for two types of models. According to the result, the artificial neural network model provided better prediction with achieved root mean square error (RMSE) of 0.264897 and 0.2951116 for training and testing respectively.</span>

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“…This approach does not allow for a double measurement to evaluate soil salinity at the same time. Our solution exploits a sensor structure's capacitive properties to respond to this need as it makes it possible to measure salinity and moisture [16,17]. Salinity sensing is based on the measurement of the electrical conductivity of the soil.…”

Section: Introductionmentioning

confidence: 99%

Toward Smart Soil Sensing in v4.0 Agriculture: A New Single-Shape Sensor for Capacitive Moisture and Salinity Measurements

Escriba

Avina-Bravo

Roux

et al. 2020

Sensors

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Modern agriculture imposes the need for better knowledge of the soil moisture content to rationalize the amount of water needed to irrigate farmlands. In this context, since current technological solutions do not correspond to the cost or use criteria, this paper presents a design for a new original capacitive bi-functional sensor to measure soil moisture and salinity. In this paper, we outline the design stages from simulation to finished elements of the optimal design to deployment in the fields, considering the mechanical integration constraints necessary for industrialization. The measurement electronics were developed based on the sensor’s electric model to obtain a double measurement. An on-site (field lot) measurement program was then carried out to validate the system’s good performance in real-time. Finally, this performance was matched with that of leading commercially available sensors on the market. This work demonstrates that, after deployment of the sensors, the overall system makes it possible to obtain a precise image of cultivated soil’s hydric condition, with the best response time.

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Transfer Function and Time Series Outlier Analysis: Modelling Soil Salinity in Loamy Sand Soil by Including the Influences of Irrigation Management and Soil Temperature

Cited by 8 publications

References 34 publications

Microbial-assisted soil chromium immobilization through zinc and iron-enriched rice husk biochar

Microbial-assisted soil chromium immobilization through zinc and iron-enriched rice husk biochar

Comparison study of transfer function and artificial neural network for cash flow analysis at Bank Rakyat Indonesia

Toward Smart Soil Sensing in v4.0 Agriculture: A New Single-Shape Sensor for Capacitive Moisture and Salinity Measurements

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